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We collected information from six rice microarray platforms, including the Affymetrix, Agilent 22K and 44K, BGI/Yale, and the NSF 20K and 45K, to construct the ROAD.
We have developed a simple probe-property-independent model, the global average model (GLAM), which we have used to predict absolute target concentration on different microarray platforms, including Affymetrix, Agilent, Illumina and a locally developed custom microarray.
We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray.
It also facilitated comparison of datasets across microarray platforms, including for identifying homologous cell types across species as we have pioneered [ 14, 24– 28].
Transcriptome analysis by this method has some significant advantages over microarray platforms, including analysis of polymorphisms in the coding sequence and the ability to detect novel transcripts.
In summary, our results showed that there were moderate, yet significant, correlations between microarray data and MPSS data, while the data from microarray platforms, including the recently included Illumina arrays, generally were well correlated.
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Many microarray platforms include multiple probes for a subset of genes.
Finally, the resulting values could be applied, in principle, to any data utilizing any of the microarray platforms included in this experiment.
The induction of TEL-AML1 results in binding to promoter regions of 2585 genes identified in ChIP experiments and also probed on the mRNA microarray platform, including granzyme B, a previously classified TEL-AML1 target.
The technology use transcript specific color coded molecular probe pairs to capture and count individual transcripts and has several advantages in comparison to microarray platform including easy protocols, less hands-on time and higher sample throughput [ 17].
The high correlation coefficients imply a high degree of technical reproducibility of the overall microarray platform, including the amplification step, and a lack of biological variation across different samples.
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Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com